class_docstr: A baseline approach for computing input attribution. It returns
  the gradients with respect to inputs. If `abs` is set to True, which is
  the default, the absolute value of the gradients is returned.
  
  More details about the approach can be found in the following paper:
      https://arxiv.org/abs/1312.6034
docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which saliency
                  is computed. If forward_func takes a single tensor
                  as input, a single input tensor should be provided.
                  If forward_func takes multiple tensors as input, a tuple
                  of the input tensors should be provided. It is assumed
                  that for all given input tensors, dimension 0 corresponds
                  to the number of examples (aka batch size), and if
                  multiple input tensors are provided, the examples must
                  be aligned appropriately.
      target (int, tuple, Tensor, or list, optional): Output indices for
                  which gradients are computed (for classification cases,
                  this is usually the target class).
                  If the network returns a scalar value per example,
                  no target index is necessary.
                  For general 2D outputs, targets can be either:
  
                  - a single integer or a tensor containing a single
                    integer, which is applied to all input examples
  
                  - a list of integers or a 1D tensor, with length matching
                    the number of examples in inputs (dim 0). Each integer
                    is applied as the target for the corresponding example.
  
                  For outputs with > 2 dimensions, targets can be either:
  
                  - A single tuple, which contains #output_dims - 1
                    elements. This target index is applied to all examples.
  
                  - A list of tuples with length equal to the number of
                    examples in inputs (dim 0), and each tuple containing
                    #output_dims - 1 elements. Each tuple is applied as the
                    target for the corresponding example.
  
                  Default: None
      abs (bool, optional): Returns absolute value of gradients if set
                  to True, otherwise returns the (signed) gradients if
                  False.
                  Default: True
      additional_forward_args (Any, optional): If the forward function
                  requires additional arguments other than the inputs for
                  which attributions should not be computed, this argument
                  can be provided. It must be either a single additional
                  argument of a Tensor or arbitrary (non-tuple) type or a
                  tuple containing multiple additional arguments including
                  tensors or any arbitrary python types. These arguments
                  are provided to forward_func in order following the
                  arguments in inputs.
                  Note that attributions are not computed with respect
                  to these arguments.
                  Default: None
  
  Returns:
      *Tensor* or *tuple[Tensor, ...]* of **attributions**:
      - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                  The gradients with respect to each input feature.
                  Attributions will always be
                  the same size as the provided inputs, with each value
                  providing the attribution of the corresponding input index.
                  If a single tensor is provided as inputs, a single tensor is
                  returned. If a tuple is provided for inputs, a tuple of
                  corresponding sized tensors is returned.
  
  
  Examples::
  
      >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
      >>> # and returns an Nx10 tensor of class probabilities.
      >>> net = ImageClassifier()
      >>> # Generating random input with size 2x3x3x32
      >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
      >>> # Defining Saliency interpreter
      >>> saliency = Saliency(net)
      >>> # Computes saliency maps for class 3.
      >>> attribution = saliency.attribute(input, target=3)
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code: def attribute(
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      target: TargetType = None,
      abs: bool = True,
      additional_forward_args: Any = None,
  ) -> TensorOrTupleOfTensorsGeneric:
      r"""
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which saliency
                      is computed. If forward_func takes a single tensor
                      as input, a single input tensor should be provided.
                      If forward_func takes multiple tensors as input, a tuple
                      of the input tensors should be provided. It is assumed
                      that for all given input tensors, dimension 0 corresponds
                      to the number of examples (aka batch size), and if
                      multiple input tensors are provided, the examples must
                      be aligned appropriately.
          target (int, tuple, Tensor, or list, optional): Output indices for
                      which gradients are computed (for classification cases,
                      this is usually the target class).
                      If the network returns a scalar value per example,
                      no target index is necessary.
                      For general 2D outputs, targets can be either:
  
                      - a single integer or a tensor containing a single
                        integer, which is applied to all input examples
  
                      - a list of integers or a 1D tensor, with length matching
                        the number of examples in inputs (dim 0). Each integer
                        is applied as the target for the corresponding example.
  
                      For outputs with > 2 dimensions, targets can be either:
  
                      - A single tuple, which contains #output_dims - 1
                        elements. This target index is applied to all examples.
  
                      - A list of tuples with length equal to the number of
                        examples in inputs (dim 0), and each tuple containing
                        #output_dims - 1 elements. Each tuple is applied as the
                        target for the corresponding example.
  
                      Default: None
          abs (bool, optional): Returns absolute value of gradients if set
                      to True, otherwise returns the (signed) gradients if
                      False.
                      Default: True
          additional_forward_args (Any, optional): If the forward function
                      requires additional arguments other than the inputs for
                      which attributions should not be computed, this argument
                      can be provided. It must be either a single additional
                      argument of a Tensor or arbitrary (non-tuple) type or a
                      tuple containing multiple additional arguments including
                      tensors or any arbitrary python types. These arguments
                      are provided to forward_func in order following the
                      arguments in inputs.
                      Note that attributions are not computed with respect
                      to these arguments.
                      Default: None
  
      Returns:
          *Tensor* or *tuple[Tensor, ...]* of **attributions**:
          - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                      The gradients with respect to each input feature.
                      Attributions will always be
                      the same size as the provided inputs, with each value
                      providing the attribution of the corresponding input index.
                      If a single tensor is provided as inputs, a single tensor is
                      returned. If a tuple is provided for inputs, a tuple of
                      corresponding sized tensors is returned.
  
  
      Examples::
  
          >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
          >>> # and returns an Nx10 tensor of class probabilities.
          >>> net = ImageClassifier()
          >>> # Generating random input with size 2x3x3x32
          >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
          >>> # Defining Saliency interpreter
          >>> saliency = Saliency(net)
          >>> # Computes saliency maps for class 3.
          >>> attribution = saliency.attribute(input, target=3)
      """
      # Keeps track whether original input is a tuple or not before
      # converting it into a tuple.
      is_inputs_tuple = _is_tuple(inputs)
  
      inputs = _format_tensor_into_tuples(inputs)
      gradient_mask = apply_gradient_requirements(inputs)
  
      # No need to format additional_forward_args here.
      # They are being formated in the `_run_forward` function in `common.py`
      gradients = self.gradient_func(
          self.forward_func, inputs, target, additional_forward_args
      )
      if abs:
          attributions = tuple(
              torch.abs(gradient) for gradient in gradients
          )
      else:
          attributions = gradients
      undo_gradient_requirements(inputs, gradient_mask)
      return _format_output(is_inputs_tuple, attributions)
  
file_path: captum/captum/attr/_core/saliency.py
imports: from typing import Any, Callable
  import torch
  from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple
  from captum._utils.gradient import apply_gradient_requirements, undo_gradient_requirements
  from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.attribution import GradientAttribution
  from captum.log import log_usage
label_desc: Use image node_id 2 for calling the Saliency obj's underlying member method code with example usage: obj.attribute(inputs, target, abs, additional_forward_args) and returns: _format_output